AIOps Lab Day-01: Detect CPU Anomalies Using Prometheus, Grafana & ML | Rajinikanth Vadla скачать в хорошем качестве

AIOps Lab Day-01: Detect CPU Anomalies Using Prometheus, Grafana & ML | Rajinikanth Vadla

github repo: https://github.com/rajinikanthvadla-ai 🎯 WANT PERSONALIZED GUIDANCE? Book 1-on-1 Sessions with Me! 👇 🔗 https://topmate.io/rajinikanthvadla 💡 Connect for mentorship, career advice, MLOps consulting & more! 🚀 Welcome to Day-01 of the AIOps Lab Series! In this video, Rajinikanth Vadla walks you through a real-time, hands-on AIOps lab for CPU anomaly detection using Prometheus, Grafana, and Machine Learning. Learn how to: ✅ Monitor CPU metrics like a pro ✅ Detect anomalies using real-world ML techniques ✅ Automate incident detection in DevOps environments ✅ Set up dashboards that tell you what’s wrong before it happens! 🔍 This lab is perfect for: DevOps Engineers SREs MLOps Professionals IT Automation Enthusiasts Anyone who wants to master AI-driven Observability & Monitoring 👨‍💻 Subscribe to stay updated with this AIOps Master Series and become a SmartOps Engineer of tomorrow. 🎯 Don’t forget to Like, Comment & Share to support my mission of teaching real-world AIOps! 📌 Subscribe for FREE Labs, Workshops & AIOps Mastery Tips:    / @rajinikanthvadla   #AIOps #DevOps #MLOps #RajinikanthVadla #Observability #AnomalyDetection #CPUUsage #Grafana #Prometheus #SmartOps #ITAutomation #AIOpsLab #AIOps #DevOps #MLOps #AnomalyDetection #CPUMonitoring #SmartOps #Grafana #Prometheus #MachineLearning #AIinOps #RajinikanthVadla #ITAutomation #Observability AIOps, DevOps, MLOps, Prometheus monitoring, Grafana dashboard, anomaly detection, CPU monitoring, AIOps tutorial, Rajinikanth Vadla, AI in DevOps, IT Automation, real-time anomaly detection, machine learning in ops, observability tools, SRE, smartops, how to detect cpu anomalies, ml based monitoring, prometheus grafana lab, AIOps explained, cpu anomaly detection, ai-driven monitoring MLOps full course, MLOps tutorial for beginners, MLOps end to end project, LLMOps tutorial, LLM deployment, ChatGPT deployment, AWS SageMaker tutorial, Azure Machine Learning, Google Cloud AI Platform, Kubernetes tutorial, Docker tutorial, MLflow tutorial, machine learning engineering, data engineering, generative AI, large language models, LLM fine tuning, prompt engineering, RAG tutorial, vector database, langchain tutorial, AWS MLOps, Azure MLOps, GCP MLOps, cloud machine learning, ML pipeline, model deployment AWS, model monitoring, CI/CD machine learning, DevOps tutorial, Python machine learning, deep learning deployment, TensorFlow serving, PyTorch deployment, model versioning, experiment tracking, feature store, data drift detection, model registry, Kubernetes MLOps, Docker MLOps, Jenkins ML, GitHub Actions ML, terraform AWS, infrastructure as code, serverless ML, AWS Lambda ML, API deployment, FastAPI ML, model optimization, quantization, ONNX, TensorRT, ML inference, batch prediction, real-time prediction, streaming ML, Apache Kafka ML, data pipeline, airflow tutorial, prefect tutorial, AWS Glue, Azure Data Factory, BigQuery ML, Snowflake ML, Databricks MLOps, Vertex AI, AWS Bedrock, Azure OpenAI, OpenAI API, Hugging Face deployment, transformers deployment, BERT deployment, stable diffusion deployment, diffusion models, computer vision deployment, NLP deployment, time series ML, MLOps certification, MLOps interview questions, MLOps best practices, MLOps architecture, MLOps tools comparison, open source MLOps, enterprise MLOps, scalable ML systems, distributed training, model serving, A/B testing ML, canary deployment, blue green deployment, shadow deployment, feature engineering, data preprocessing, model evaluation, hyperparameter tuning, AutoML, neural architecture search, edge ML deployment, IoT ML, embedded ML, MLOps 2025, latest MLOps trends, GenAI MLOps